{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52510cb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>4957.98</td>\n",
       "      <td>4917.77</td>\n",
       "      <td>4961.45</td>\n",
       "      <td>4874.53</td>\n",
       "      <td>151534776.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>4907.93</td>\n",
       "      <td>4868.12</td>\n",
       "      <td>4916.28</td>\n",
       "      <td>4851.98</td>\n",
       "      <td>178816100.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>4842.16</td>\n",
       "      <td>4818.23</td>\n",
       "      <td>4857.56</td>\n",
       "      <td>4786.43</td>\n",
       "      <td>157665826.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-07</td>\n",
       "      <td>4824.32</td>\n",
       "      <td>4822.37</td>\n",
       "      <td>4856.65</td>\n",
       "      <td>4818.19</td>\n",
       "      <td>187139413.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-10</td>\n",
       "      <td>4812.23</td>\n",
       "      <td>4844.05</td>\n",
       "      <td>4844.39</td>\n",
       "      <td>4780.82</td>\n",
       "      <td>156211696.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-01-11</td>\n",
       "      <td>4840.01</td>\n",
       "      <td>4797.77</td>\n",
       "      <td>4850.88</td>\n",
       "      <td>4791.89</td>\n",
       "      <td>146928004.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>4818.89</td>\n",
       "      <td>4845.58</td>\n",
       "      <td>4852.73</td>\n",
       "      <td>4810.92</td>\n",
       "      <td>143200204.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>4853.03</td>\n",
       "      <td>4765.92</td>\n",
       "      <td>4853.06</td>\n",
       "      <td>4765.24</td>\n",
       "      <td>142519653.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-01-14</td>\n",
       "      <td>4743.91</td>\n",
       "      <td>4726.73</td>\n",
       "      <td>4764.05</td>\n",
       "      <td>4723.94</td>\n",
       "      <td>146863126.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-01-17</td>\n",
       "      <td>4728.76</td>\n",
       "      <td>4767.28</td>\n",
       "      <td>4775.24</td>\n",
       "      <td>4726.71</td>\n",
       "      <td>115295691.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-01-18</td>\n",
       "      <td>4766.78</td>\n",
       "      <td>4813.35</td>\n",
       "      <td>4826.30</td>\n",
       "      <td>4747.92</td>\n",
       "      <td>150870802.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022-01-19</td>\n",
       "      <td>4815.81</td>\n",
       "      <td>4780.38</td>\n",
       "      <td>4828.75</td>\n",
       "      <td>4755.93</td>\n",
       "      <td>133927360.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-01-20</td>\n",
       "      <td>4778.26</td>\n",
       "      <td>4823.51</td>\n",
       "      <td>4845.00</td>\n",
       "      <td>4777.57</td>\n",
       "      <td>164971315.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-01-21</td>\n",
       "      <td>4808.77</td>\n",
       "      <td>4779.31</td>\n",
       "      <td>4818.31</td>\n",
       "      <td>4762.56</td>\n",
       "      <td>138952585.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>4753.94</td>\n",
       "      <td>4786.74</td>\n",
       "      <td>4801.10</td>\n",
       "      <td>4746.45</td>\n",
       "      <td>114463880.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>4761.95</td>\n",
       "      <td>4678.45</td>\n",
       "      <td>4781.39</td>\n",
       "      <td>4678.25</td>\n",
       "      <td>133667586.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>4697.10</td>\n",
       "      <td>4712.31</td>\n",
       "      <td>4718.99</td>\n",
       "      <td>4648.13</td>\n",
       "      <td>107070842.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-01-27</td>\n",
       "      <td>4708.10</td>\n",
       "      <td>4619.88</td>\n",
       "      <td>4708.44</td>\n",
       "      <td>4616.03</td>\n",
       "      <td>121263804.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>4641.81</td>\n",
       "      <td>4563.77</td>\n",
       "      <td>4660.52</td>\n",
       "      <td>4559.83</td>\n",
       "      <td>126480112.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>4638.58</td>\n",
       "      <td>4634.09</td>\n",
       "      <td>4672.65</td>\n",
       "      <td>4615.01</td>\n",
       "      <td>144849720.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-02-08</td>\n",
       "      <td>4626.44</td>\n",
       "      <td>4608.77</td>\n",
       "      <td>4626.44</td>\n",
       "      <td>4522.47</td>\n",
       "      <td>155830708.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-02-09</td>\n",
       "      <td>4608.15</td>\n",
       "      <td>4652.06</td>\n",
       "      <td>4659.46</td>\n",
       "      <td>4595.28</td>\n",
       "      <td>148335547.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-02-10</td>\n",
       "      <td>4657.21</td>\n",
       "      <td>4639.86</td>\n",
       "      <td>4657.21</td>\n",
       "      <td>4608.09</td>\n",
       "      <td>151576666.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-02-11</td>\n",
       "      <td>4617.87</td>\n",
       "      <td>4601.40</td>\n",
       "      <td>4662.60</td>\n",
       "      <td>4595.88</td>\n",
       "      <td>155974584.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022-02-14</td>\n",
       "      <td>4579.42</td>\n",
       "      <td>4551.69</td>\n",
       "      <td>4593.63</td>\n",
       "      <td>4530.06</td>\n",
       "      <td>143673894.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>4553.81</td>\n",
       "      <td>4600.10</td>\n",
       "      <td>4601.45</td>\n",
       "      <td>4550.54</td>\n",
       "      <td>115025600.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022-02-16</td>\n",
       "      <td>4619.85</td>\n",
       "      <td>4617.99</td>\n",
       "      <td>4643.10</td>\n",
       "      <td>4607.22</td>\n",
       "      <td>107258955.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>4616.90</td>\n",
       "      <td>4629.16</td>\n",
       "      <td>4650.16</td>\n",
       "      <td>4606.95</td>\n",
       "      <td>110014643.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2022-02-18</td>\n",
       "      <td>4604.57</td>\n",
       "      <td>4651.24</td>\n",
       "      <td>4651.34</td>\n",
       "      <td>4598.10</td>\n",
       "      <td>104550228.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2022-02-21</td>\n",
       "      <td>4646.05</td>\n",
       "      <td>4634.31</td>\n",
       "      <td>4646.67</td>\n",
       "      <td>4613.30</td>\n",
       "      <td>112516512.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>4601.92</td>\n",
       "      <td>4574.15</td>\n",
       "      <td>4602.07</td>\n",
       "      <td>4550.80</td>\n",
       "      <td>123317237.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2022-02-23</td>\n",
       "      <td>4582.01</td>\n",
       "      <td>4623.05</td>\n",
       "      <td>4625.30</td>\n",
       "      <td>4579.29</td>\n",
       "      <td>122952916.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>4592.07</td>\n",
       "      <td>4529.32</td>\n",
       "      <td>4610.88</td>\n",
       "      <td>4488.48</td>\n",
       "      <td>184403058.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>4564.63</td>\n",
       "      <td>4573.42</td>\n",
       "      <td>4611.86</td>\n",
       "      <td>4561.90</td>\n",
       "      <td>149722583.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>4563.74</td>\n",
       "      <td>4581.65</td>\n",
       "      <td>4581.65</td>\n",
       "      <td>4530.68</td>\n",
       "      <td>135406978.0</td>\n",
       "      <td>hs300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date     open    close     high      low       volume   code\n",
       "0   2022-01-04  4957.98  4917.77  4961.45  4874.53  151534776.0  hs300\n",
       "1   2022-01-05  4907.93  4868.12  4916.28  4851.98  178816100.0  hs300\n",
       "2   2022-01-06  4842.16  4818.23  4857.56  4786.43  157665826.0  hs300\n",
       "3   2022-01-07  4824.32  4822.37  4856.65  4818.19  187139413.0  hs300\n",
       "4   2022-01-10  4812.23  4844.05  4844.39  4780.82  156211696.0  hs300\n",
       "5   2022-01-11  4840.01  4797.77  4850.88  4791.89  146928004.0  hs300\n",
       "6   2022-01-12  4818.89  4845.58  4852.73  4810.92  143200204.0  hs300\n",
       "7   2022-01-13  4853.03  4765.92  4853.06  4765.24  142519653.0  hs300\n",
       "8   2022-01-14  4743.91  4726.73  4764.05  4723.94  146863126.0  hs300\n",
       "9   2022-01-17  4728.76  4767.28  4775.24  4726.71  115295691.0  hs300\n",
       "10  2022-01-18  4766.78  4813.35  4826.30  4747.92  150870802.0  hs300\n",
       "11  2022-01-19  4815.81  4780.38  4828.75  4755.93  133927360.0  hs300\n",
       "12  2022-01-20  4778.26  4823.51  4845.00  4777.57  164971315.0  hs300\n",
       "13  2022-01-21  4808.77  4779.31  4818.31  4762.56  138952585.0  hs300\n",
       "14  2022-01-24  4753.94  4786.74  4801.10  4746.45  114463880.0  hs300\n",
       "15  2022-01-25  4761.95  4678.45  4781.39  4678.25  133667586.0  hs300\n",
       "16  2022-01-26  4697.10  4712.31  4718.99  4648.13  107070842.0  hs300\n",
       "17  2022-01-27  4708.10  4619.88  4708.44  4616.03  121263804.0  hs300\n",
       "18  2022-01-28  4641.81  4563.77  4660.52  4559.83  126480112.0  hs300\n",
       "19  2022-02-07  4638.58  4634.09  4672.65  4615.01  144849720.0  hs300\n",
       "20  2022-02-08  4626.44  4608.77  4626.44  4522.47  155830708.0  hs300\n",
       "21  2022-02-09  4608.15  4652.06  4659.46  4595.28  148335547.0  hs300\n",
       "22  2022-02-10  4657.21  4639.86  4657.21  4608.09  151576666.0  hs300\n",
       "23  2022-02-11  4617.87  4601.40  4662.60  4595.88  155974584.0  hs300\n",
       "24  2022-02-14  4579.42  4551.69  4593.63  4530.06  143673894.0  hs300\n",
       "25  2022-02-15  4553.81  4600.10  4601.45  4550.54  115025600.0  hs300\n",
       "26  2022-02-16  4619.85  4617.99  4643.10  4607.22  107258955.0  hs300\n",
       "27  2022-02-17  4616.90  4629.16  4650.16  4606.95  110014643.0  hs300\n",
       "28  2022-02-18  4604.57  4651.24  4651.34  4598.10  104550228.0  hs300\n",
       "29  2022-02-21  4646.05  4634.31  4646.67  4613.30  112516512.0  hs300\n",
       "30  2022-02-22  4601.92  4574.15  4602.07  4550.80  123317237.0  hs300\n",
       "31  2022-02-23  4582.01  4623.05  4625.30  4579.29  122952916.0  hs300\n",
       "32  2022-02-24  4592.07  4529.32  4610.88  4488.48  184403058.0  hs300\n",
       "33  2022-02-25  4564.63  4573.42  4611.86  4561.90  149722583.0  hs300\n",
       "34  2022-02-28  4563.74  4581.65  4581.65  4530.68  135406978.0  hs300"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "hs300=ts.get_k_data('hs300',start='2022-01-01',end='2022-02-29')\n",
    "hs300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b7f4c6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                     浙江众成\n",
       "industry                   塑料\n",
       "area                       浙江\n",
       "pe                      52.29\n",
       "outstanding              8.55\n",
       "totals                   9.06\n",
       "totalAssets         316361.13\n",
       "liquidAssets        139801.88\n",
       "fixedAssets         123311.22\n",
       "reserved             63046.65\n",
       "reservedPerShare          0.7\n",
       "esp                     0.049\n",
       "bvps                     1.99\n",
       "pb                        2.6\n",
       "timeToMarket         20101210\n",
       "undp                 17748.48\n",
       "perundp                   0.2\n",
       "rev                     17.57\n",
       "profit                  79.12\n",
       "gpr                     25.94\n",
       "npr                      7.88\n",
       "holders                 33327\n",
       "Name: 002522, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "df=pd.read_excel ('stock.xlsx',dtype={'code':'str'})\n",
    "df.set_index('code',inplace=True)\n",
    "df.loc['002522']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72c98d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.industry.unique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c574292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.area.unique())  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09f8d29e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "浙江     445\n",
       "江苏     414\n",
       "北京     332\n",
       "广东     313\n",
       "上海     297\n",
       "深圳     290\n",
       "山东     205\n",
       "福建     136\n",
       "四川     124\n",
       "湖南     104\n",
       "湖北     103\n",
       "安徽     103\n",
       "河南      79\n",
       "辽宁      74\n",
       "河北      57\n",
       "新疆      55\n",
       "陕西      52\n",
       "天津      51\n",
       "重庆      50\n",
       "江西      42\n",
       "吉林      41\n",
       "黑龙江     38\n",
       "广西      38\n",
       "山西      37\n",
       "云南      36\n",
       "甘肃      32\n",
       "海南      31\n",
       "贵州      29\n",
       "内蒙      25\n",
       "西藏      18\n",
       "宁夏      14\n",
       "青海      12\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').area.count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61962a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'年IPO数量'}, xlabel='timeToMarket'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = df.timeToMarket.astype('str').str[:4] \n",
    "yearnum = df.groupby(year).name.count()\n",
    "\n",
    "yearnum[yearnum.index!='0'].plot(fontsize=14, title='年IPO数量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a56db9dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe均值</th>\n",
       "      <th>股票数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>board</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00</th>\n",
       "      <td>63.893655</td>\n",
       "      <td>1398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>115.524429</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>83.506167</td>\n",
       "      <td>1482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>215.954643</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pe均值   股票数\n",
       "board                  \n",
       "00      63.893655  1398\n",
       "30     115.524429   770\n",
       "60      83.506167  1482\n",
       "68     215.954643    28"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pe.mean()       \n",
    "df[df.pe > 0].pe.mean() \n",
    "df['tvalue'] = 4 * df.esp * df.pe * df.totals  \n",
    "np.sum(df.pe * df.tvalue) / df.tvalue.sum()\n",
    "df['board'] = df.index.str[:2]\n",
    "df.groupby('board').pe.agg([('pe均值', 'mean'), ('股票数', 'count')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4febc9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
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